Introduction to RAPIDS | NVIDIA

Accredian Publication
2 min readJan 27, 2022

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Introduction

GPUs have been very popular in the gaming industry because of their fast rendering and processing of computer games. Originally, they were designed to render graphics through rapid mathematical calculations, and these days they are being used to process large amounts of data for machine learning and deep learning processes.

In Data Science, Python libraries have improved and become increasingly efficient concerning CPUs. However, if you want to process billions of bytes of data, CPUs will not be sufficient. GPUs with their powerful parallel architecture allow organizations to plan, execute, and optimize processes using an abundance of data referred to as Big Data these days.

About RAPIDS

RAPIDS is a suite of open-source software libraries and APIs that gives the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. It has been licensed under Apache 2.0 and incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

This software also focuses on common data preparation tasks for analytics and data science including a familiar data frame API that integrates with a variety of machine learning algorithms. They support end-to-end pipeline accelerations without paying typical serialization costs. In addition, they also support multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

Features of RAPIDS

· Hassle-Free Integration: Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

· Reduced Training Time: Drastically improve your productivity with near-interactive data science.

· Top Model Accuracy: Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

· Open Source: Customizable, extensible, interoperable — the open-source software is supported by NVIDIA and built on Apache Arrow.

What’s next?

Setting up Rapids AI in Google Collaboratory

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